Further Developments Towards a Genome-scale Metabolic Model of Yeast

Overview

Journal BMC Syst Biol

Publisher Biomed Central

Specialty Biology

Date 2010 Oct 30

PMID 21029416

Citations 45

Authors

Paul D Dobson

Kieran Smallbone

Daniel Jameson

Evangelos Simeonidis

Karin Lanthaler

Pinar Pir

Chuan Lu

Neil Swainston

Warwick B Dunn

Paul Fisher

Duncan Hull

Marie Brown

Olusegun Oshota

Natalie J Stanford

Douglas B Kell

Ross D King

Stephen G Oliver

Robert D Stevens

Pedro Mendes

Affiliations

Soon will be listed here.

Abstract

Background: To date, several genome-scale network reconstructions have been used to describe the metabolism of the yeast Saccharomyces cerevisiae, each differing in scope and content. The recent community-driven reconstruction, while rigorously evidenced and well annotated, under-represented metabolite transport, lipid metabolism and other pathways, and was not amenable to constraint-based analyses because of lack of pathway connectivity.

Results: We have expanded the yeast network reconstruction to incorporate many new reactions from the literature and represented these in a well-annotated and standards-compliant manner. The new reconstruction comprises 1102 unique metabolic reactions involving 924 unique metabolites--significantly larger in scope than any previous reconstruction. The representation of lipid metabolism in particular has improved, with 234 out of 268 enzymes linked to lipid metabolism now present in at least one reaction. Connectivity is emphatically improved, with more than 90% of metabolites now reachable from the growth medium constituents. The present updates allow constraint-based analyses to be performed; viability predictions of single knockouts are comparable to results from in vivo experiments and to those of previous reconstructions.

Conclusions: We report the development of the most complete reconstruction of yeast metabolism to date that is based upon reliable literature evidence and richly annotated according to MIRIAM standards. The reconstruction is available in the Systems Biology Markup Language (SBML) and via a publicly accessible database http://www.comp-sys-bio.org/yeastnet/.

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